Recognition of Plant Syndrome Using Image Processing Techniques

Plant pathologists detect syndromes directly with the naked eye. However, such detection usually requires continuous monitoring, which is time overwhelming and very expensive on large farms. The disease diagnosis is limited by the human’s visual abilities because most of the first symptoms are microscopic. Therefore, seeking rapid, automated, economical, and accurate methods of plant syndrome detection is very important .This project deals with the image processing techniques, used to identify and classify the disease symptoms affected on different agriculture crops. Plant diseases are mainly caused by bacteria, fungi, virus, nematodes, etc., of which fungi is the main infection causing creature. The magnitude and eminence of plant products gets reduced by plant diseases. The goal is to detect, to categorize and to accurately quantify the main symptoms of plant diseases using the disease affected leaf image by extracting the features like MSERF and SURF of diseased leaf part which is useful for the classification of syndromes.